Progressive Web Application for Plant Disease Detection using CNN

A large portion of India's population relies primarily on agriculture for their livelihood. Farmers suffer a considerable amount of loss due to the innumerable diseases affecting their plants. Detection of such plant diseases with the human eye often yields inaccurate results. Furthermore, to correctly identify the disease, the individual assessing the plant should be an expert in the respective field. The diagnosis of plant illness is a visual task and thus, many computer vision techniques have been used previously for tackling it. Recently, convolutional Neural Networks have shown excellent results in many computer vision tasks. This study develops an application for plant disease classification by comparing the results obtained by training two convolutional neural networks, one from scratch and one by the transfer learning method. Both achieved a validation accuracy of 86 percent and 96 percent, respectively. The system was developed in the form of a web application for both mobile and web devices using the model, which is capable of functioning without any network requirements.